In \(MCR_{mp}\) , we were able to achieve comparable results with SR-LSTM [1]}. They rely on sharing states between pedestrians using a message-passing mechanism that carries out the social information between pedestrian nodes in a global manner, meaning that they assume a fully-connected graph to refine the social interaction modeling through graphs. In Table REF , by comparing SR-LSTM to our message-passing model \(MCR_{mp}\) , we observe that both achieve approximate prediction errors, yet SR-LSTM induce higher message passing complexity due to using fully-connected graph and performing message passes pedestrian-wise to refine the social features representation.
On the other hand, \(MCR_{mp}\)  encodes pedestrians awareness states into their social interaction using multiplication as previously discussed in Section REF  without the need for iterative message-passing throughout the nodes. \(MCR_{mp}\)  model has better performance over the SR-LSTM method in Univ subset. The density of social interactions is the highest in Univ scenario with less impact from the static context on pedestrian paths. This is where the addition of social cues shows improvement in a highly interactive crowd, in comparison with the less interactive crowd as in Zara sets. The crowd in Zara is significantly influenced by the shop facade and therefore encoding the physical context features was more beneficial under these two subsets as we observe from \(MCR_{mpc}\)  performance.
